VOLUME 11, ISSUE 12, DECEMBER 2023
Battery State of Charge Estimation Methods – A Critical Review
P. R. Dhabe, Dr. S. R. Paraskar, Dr. S. S. Jadhao
HOME AUTOMATION USING RASPBERRY PI
MOHANA GEETHA, NAVEENA PRINCE A, PRAVEEN SAGAR V, RACHANA S
Automatic Generation Control of Micro Grid Interconnected Power System Using GWO
T.Kranti Kiran, A.Durga Prasad, M.Mallikarjuna Reddy, B.Samba Siva Rao
Automated Guided Vehicle Systems Designing & Development
Mr.Rahul B. Chandrayan
Waste heat energy harvesting using thermoelectric generation
Sonali Rathod, Ajay Shelke, Dhananjay Khandre, Sneha Hagavane, Prof. Snehal Andhale
Visible Light Communication for Wireless Networking: Li-Fi Technology
Gurleen Kaur, Baljeet Kaur, Gurpurneet kaur
Lithium And its Recycling Technology- A Brief Review
Hariprasad C, Ashwin P, Sampatkumar Pandurang Naik, Dr. Neelakantha V L
IoT-BASED BIOGAS LEAKAGE-DETECTING DEVICE
Dr.R.Jayakarthik, Dr.JayanthilaDevi
Deploying Generative AI and Cloud-Based Tools to Enhance Renewable Energy Education Platforms
Venkata Narasareddy Annapareddy
Enhancing Retail Infrastructure Agility through Intelligent OSS and ML-Orchestrated Workflows
Shabrinath Motamary
Leveraging AI and Machine Learning for Dynamic Risk Assessment in Auto and Property Insurance Markets
Lahari Pandiri
Leveraging Artificial Intelligence for Strategic Decision-Making in Tax Administration and Policy Design
Vamsee Pamisetty
AI-Driven Cloud Computing for Personalized Learning Platforms
Anumandla Mukesh
Federated Learning and Cloud-Based Artificial Intelligence for Real-Time Diagnosis of Rare Diseases in Healthcare Systems
Ghatoth mishra
Abstract
Battery State of Charge Estimation Methods – A Critical Review
P. R. Dhabe, Dr. S. R. Paraskar, Dr. S. S. Jadhao
DOI: 10.17148/IJIREEICE.2023.111201
Keywords: Battery Management System (BMS), State of Charge (SoC), Electric Vehicle (EV), Look-up table based estimation, model based estimation, adaptive system based estimation.
Abstract
HOME AUTOMATION USING RASPBERRY PI
MOHANA GEETHA, NAVEENA PRINCE A, PRAVEEN SAGAR V, RACHANA S
DOI: 10.17148/IJIREEICE.2023.111202
Keywords: Internet of Things, Raspberry Pi 4, Home automation, Server, Mobile devices.
Abstract
Automatic Generation Control of Micro Grid Interconnected Power System Using GWO
T.Kranti Kiran, A.Durga Prasad, M.Mallikarjuna Reddy, B.Samba Siva Rao
DOI: 10.17148/IJIREEICE.2023.111203
Keywords: Micro grid, Load frequency control, Grey wolf optimization algorithm, PI/PID controllers.
Abstract
Wheelchair Controlled By Head Motion
HAMSA REKHA S D, SHEETAL N
DOI: 10.17148/IJIREEICE.2023.111204
Keywords: Head motion, Quadriplegics, IoT, Wheelchair.
Abstract
Automated Guided Vehicle Systems Designing & Development
Mr.Rahul B. Chandrayan
DOI: 10.17148/IJIREEICE.2023.111205
Keywords: ATMEGA 328, Software, AGVs, Material handling system, Automation.
Abstract
Waste heat energy harvesting using thermoelectric generation
Sonali Rathod, Ajay Shelke, Dhananjay Khandre, Sneha Hagavane, Prof. Snehal Andhale
DOI: 10.17148/IJIREEICE.2023.111206
Abstract
Visible Light Communication for Wireless Networking: Li-Fi Technology
Gurleen Kaur, Baljeet Kaur, Gurpurneet kaur
DOI: 10.17148/IJIREEICE.2023.111207
The present project involves the practical implementation of Li-Fi, where data is transmitted up to a distance of 2 - 3 meter, achieving a data rate of 115200 bits per second with a speed of 14 kbps.
Keywords: LED, Li-Fi, Visible light communication, Alternating current, Direct current, Printed circuit board.
Abstract
Lithium And its Recycling Technology- A Brief Review
Hariprasad C, Ashwin P, Sampatkumar Pandurang Naik, Dr. Neelakantha V L
DOI: 10.17148/IJIREEICE.2023.111208
Abstract
IoT-BASED BIOGAS LEAKAGE-DETECTING DEVICE
Dr.R.Jayakarthik, Dr.JayanthilaDevi
DOI: 10.17148/IJIREEICE.2023.111209
Keywords: IoT, Biogas, Sensors, Gas Sensor, Leakage Detecting Device
Abstract
Deploying Generative AI and Cloud-Based Tools to Enhance Renewable Energy Education Platforms
Venkata Narasareddy Annapareddy
DOI: 10.17148/IJIREEICE.2023.111210
Keywords: Generative AI, cloud-based tools, renewable energy education, digital learning platforms, AI-driven content creation, interactive simulations, real-time data analysis, personalized learning, smart grid education, machine learning, energy forecasting, remote labs, scalable infrastructure, virtual classrooms, adaptive learning systems, data visualization, educational technology, sustainable energy training, cloud computing, intelligent tutoring systems.
Abstract
Enhancing Retail Infrastructure Agility through Intelligent OSS and ML-Orchestrated Workflows
Shabrinath Motamary
DOI: 10.17148/IJIREEICE.2023.111211
The introduction of intelligent operational support systems redefines the traditional retail operational framework, offering capabilities that extend beyond basic system support functions. These systems, equipped with advanced algorithms, provide real-time data processing, predictive analytics, and autonomous adjustments that are instrumental in decision- making processes. By leveraging machine learning, retailers can orchestrate workflows that automatically adapt to fluctuating demands and optimize inventory management while minimizing human intervention and operational bottlenecks. This not only enhances agility within supply chains but also empowers retailers to offer personalized customer experiences by understanding consumer behaviors and preferences with greater precision.
Furthermore, the adoption of machine learning-orchestrated workflows facilitates a more proactive retail strategy, allowing organizations to anticipate market trends and respond swiftly to external disruptions. It fosters a data-driven culture where insights are continuously derived from intricate datasets, enabling strategic planning and nimble execution. As retail establishments evolve into complex ecosystems, the intelligent operational support systems framework emerges as a critical component for sustaining competitive advantage and driving sustainable growth. This text argues that the integration of these technologies into retail infrastructures is not merely beneficial but essential for remaining relevant and resilient in a fluctuating global market.
Keywords: Retail Infrastructure,Operational Support Systems (OSS),Machine Learning (ML),Workflow Automation,Infrastructure Agility,Intelligent Operations,Digital Transformation,Predictive Analytics,Retail Technology,AI-Driven Workflows,Service Orchestration,Cloud-native OSS,Network Optimization,Real-time Monitoring,Scalable Retail Solutions.
Abstract
Leveraging AI and Machine Learning for Dynamic Risk Assessment in Auto and Property Insurance Markets
Lahari Pandiri
DOI: 10.17148/IJIREEICE.2023.111212
The integration of AI and ML into risk assessment processes empowers insurers to leverage predictive analytics, deriving insights that improve decision-making accuracy. For instance, in auto insurance, telematics combined with AI allows for refined driver behavior analysis, adjusting premiums based on real-time driving patterns rather than generic demographic information. Similarly, ML models in property insurance utilize data from various sources, including IoT devices and satellite imagery, to dynamically update risk assessments for properties based on environmental changes and historical weather patterns. Such capabilities not only enhance precision but also foster proactive risk management.
Moreover, the implementation of AI and ML introduces a paradigm shift towards personalization in insurance products and services, aligning closely with individual risk factors and preferences. The predictive power of these technologies facilitates the identification of potential fraud, optimizing claims processing and reducing operational costs. However, the adoption of AI-driven risk assessment also brings challenges, including data privacy concerns and the need for robust regulatory frameworks to govern AI applications in insurance. This analysis underscores the need for insurers to balance technological advancement with ethical considerations and regulatory compliance to leverage AI's full potential responsibly. This comprehensive evaluation highlights the transformative impact of AI and ML in reshaping the landscape of dynamic risk assessment in the insurance sector.
Keywords: AI-driven risk modeling,Machine learning insurance analytics,Predictive underwriting models,Telematics data analysis,Real-time risk assessment,Property damage prediction,Automated claims processing,Fraud detection algorithms,Behavioral risk profiling,Geo-spatial risk modeling,Climate risk analytics,Dynamic pricing algorithms,Smart sensor data integration,Insurance AI decision support,Claims severity prediction.
Abstract
Leveraging Artificial Intelligence for Strategic Decision-Making in Tax Administration and Policy Design
Vamsee Pamisetty
DOI: 10.17148/IJIREEICE.2023.111213
Keywords: Artificial Intelligence, Strategic Decision-Making, Tax Administration, Policy Design, Data Analytics, Machine Learning, Predictive Modeling, Automation, Risk Assessment, Tax Compliance, Revenue Forecasting, Fraud Detection, Natural Language Processing, Data-Driven Insights, Real-Time Monitoring, Taxpayer Behavior, Decision Support Systems, Algorithmic Optimization, Policy Simulation, Digital Transformation, Tax System Efficiency, AI Governance, Intelligent Systems, Behavioral Analytics, Public Sector Innovation, Regulatory Compliance.
Abstract
Healthcare System Readiness for Artificial Intelligence Integration
Dileep Valiki
DOI: 10.17148/IJIREEICE.2023.111214
While Technology Adoption Models assess potential uptake of AI solutions by health practitioners, these validations assess preparedness to adopt and/or innovate in AI solutions within the healthcare ecosystem. Emerging AI applications covering clinical needs from radiology to mental health support, or from product development to operational management, present recognition and acceptance challenges requiring stakeholder engagement. Stakeholder impact on the success of AI solutions can be assessed using readiness levels, grouping stakeholders into three categories: IDPC leaders responsible for implementation, users at the point of care who interact directly with patients, and patients, the users of the healthcare system and the ultimate beneficiaries or sufferers from the role out of the AI solution.
Keywords: AI readiness assessment, Digital health infrastructure,Clinical workflow integration,Health data interoperability,Electronic health record (EHR) maturity,Data governance frameworks,Ethical AI in healthcare,Workforce AI competency,Change management in healthcare,Clinical decision support systems,Health information security,Regulatory compliance for AI,Algorithm transparency,Patient data quality,Organizational readiness for AI,AI adoption barriers,Health system innovation capacity,Human–AI collaboration,AI-enabled care delivery,Implementation science for AI in healthcare.
Abstract
AI-Driven Claims Processing Systems in Insurance Companies
Ganesh Pambala
DOI: 10.17148/IJIREEICE.2023.111215
But increasing the efficiency of claims processing is only half the story. Unless these savings translate into improved customer experience and satisfaction, the long-term gains may be illusory. Customers today demand – and expect – faster claims settlements. Organisations that can consistently meet these expectations are likely to enjoy a competitive edge, gaining not only customer satisfaction but also long-term loyalty. Conversely, companies that fail to deliver speedy settlements will, over time, see an increased rate of customer attrition, particularly among their high-value clients. Insurers that can harness new AI capabilities to manage these twin objectives – faster settlement with greater consistency – stand to capture both the operating and customer experience benefits.
Keywords: Artificial Intelligence In Insurance, Automated Claims Processing, Insurance Operations Optimization, Cost Reduction Strategies, Customer Experience Enhancement, Claims Settlement Acceleration, Ethical AI Governance, AI- Driven Decision Support, Small Claims Automation, Operational Efficiency Gains, Customer Satisfaction Management, Competitive Advantage In Insurance, Claims Accuracy And Consistency, Service Quality Improvement, Customer Retention And Loyalty, High-Value Client Management, Digital Insurance Transformation, AI Monitoring And Oversight, Intelligent Claims Workflows, Experience-Driven Insurance Services.
Abstract
AI-Driven Cloud Computing for Personalized Learning Platforms
Anumandla Mukesh
DOI: 10.17148/IJIREEICE.2023.111216
Adaptive education provides the theoretical foundations for personalized learning; however, student modeling, recommendation, and natural language processing remain prevalent areas for investigation. Quality and evaluation are equally important, encompassing experimental design and validation in AI-supported cloud ecosystems, along with considerations of bias, fairness, and transparency. Security, privacy, and compliance aspects of personalized learning in the cloud, including identity and data protection, risk management, auditing, and adherence to privacy frameworks such as FERPA, also warrant rigorous scrutiny.
Keywords: AI, Personalized Learning, Adaptive Learning, Cloud Computing, Technology Enhanced Learning, Educational Data Mining, Learning Analytics, Recommender Systems, Learning-as-a-Service, Learning Management System, Natural Language Processing, Evidence-based Education, Online Learning, Educational Technology, Cloud Computing, Educational Technology Evaluation, Process Mining, Artificial Intelligence in Education.
Abstract
Cloud and AI Solutions for Predictive Maintenance in Industries
Ganesh Pambala
DOI: 10.17148/IJIREEICE.2023.111217
Automation at scale is an ambitious goal that requires specialized frameworks and technologies across different areas of data engineering. These areas are outlined through recurring architectural patterns, and each pattern is built by assembling the most suitable services and tools on the market from the cloud providers that best match the organization’s business requirements in order to enable the core automation processes. Reusable building blocks are introduced for key activities such as cloud-native data platforms, data orchestration and workflow automation, automated schema discovery and adaptation, and anomaly detection and data quality alerting. Even though these solutions are presented in the context of personalized experiences and recommendation engines—typical workloads of any large e-commerce organization—they cover only part of the actual automation. The presented approaches can be applied to any AI/ML problem requiring a data plane—such as dynamic pricing and demand forecasting—with the required effort range for implementation.
Keywords: Data Engineering, Automation, AI, E-Commerce, Personalization,Automated Data Pipelines,AI-Driven ETL / ELT,Real-Time Data Processing,E-Commerce Data Integration,Data Quality Monitoring,Intelligent Data Orchestration,Predictive Data Validation,Customer Behavior Analytics,Scalable Cloud Data Warehousing,Anomaly Detection in Data Streams.
Abstract
Federated Learning and Cloud-Based Artificial Intelligence for Real-Time Diagnosis of Rare Diseases in Healthcare Systems
Ghatoth mishra
DOI: 10.17148/IJIREEICE.2023.111218
The proposed research framework guarantees that highly sensitive data from different locations remains on-site during the training process, can receive real-time predictions through the AI model of other sites, and thus supports local specialists in correctly diagnosing rare diseases. A federated approach minimizes the potential presence of low-quality data and enhances the diagnostic reliability of models used to support the decision-making process. Given the richness of medical data from different areas supplied by different medical centers, the approach is applicable across a broad range of federated-learning scenarios.
Keywords: Rare Disease Diagnosis, Federated Learning in Healthcare, Privacy-Preserving Medical AI, Distributed Clinical Decision Support, Real-Time Diagnostic Services, Multi-Institutional AI Architectures, Collaborative Model Training, Sensitive Medical Data Protection, Cloud-Based Artificial Intelligence, Federated Diagnostic Frameworks, Clinical Decision Support Systems (CDSS), Cross-Site Medical Learning, Diagnostic Reliability Enhancement, Low- Prevalence Disease Detection, Medical Data Heterogeneity, Secure AI Model Exchange, Hospital-Based AI Deployment, Federated Prediction Services, Trustworthy Medical AI, Scalable Federated Healthcare Systems.
